Florent Duculty

Free-form anisotropy: A new method for crack detection on pavement surface images

This paper presents a new measure which takes into accounts simultaneously brightness and connectivity, in the segmentation step, for crack detection on road pavement images. Features which are calculated along every free-form paths provide detection of cracks with any form and any orientation. The method proposed does not need learning stage of free defect texture to perform default detection. Experimental results were conducted on some samples of different kinds of pavements. Results of the method are also given on other kinds of images and can provide perspectives on other domains as road extraction on satellite images or segment blood vessels in retinal images.

2D image based road pavement crack detection by calculating minimal paths and dynamic programming

Road distress needs to be detected early to optimize road maintenance cost; automatic survey of road distress is a big challenge, particularity for the detection of tiny cracks due to important variation of pavement textures. This paper presents a new method for crack detection by finding the minimal path passing on each pixel of image from every path with a length d; we propose also a dynamic programming implementation to make it applicable in real condition. Methods are tested on synthesis images set and a large set of real images. Results show that cracks as small as 2mm could be detected.

Maintenance policy: degradation laws versus hidden Markov model availability indicator

Today, maintenance strategies and their analyses remain a worrying problem for companies. Socio-economic stakes depending on the competitiveness of each strategy are more than ever linked to the activity and quality of maintenance interventions. A series of specific events can eventually warn the expert of an imminent breakdown. This study aims at understanding such a signature thanks to hidden Markov models. For that purpose, two methods for damage level estimation of a maintained system are proposed. The first consists in using non-parametric and semi-parametric degradation laws (which will be used as references). The second method consists in using a Markovian approach. All proposals are illustrated on two studies corresponding to two real industrial situations (a continuous system for food processing and moulded products in aluminium alloys for the automotive industry).

Use of HMM for evaluation of maintenance activities

This paper deals with a tool which may help maintenance managers to schedule maintenance activities. To help them, we show that by using events which can be observed on a process, like maintenance events, we can predict failures before they occur. Principles are based on hypothesis that failure is preceded by a typical sequence of events. We also show that hidden Markov models can be used according to the right choice of parameters.

Failure Event Prediction Using Hidden Markov Model Approaches

In the past years, Hidden Markov Models have been used in several fields and applications with success. More recently, these models have been applied to improve the reliability of a machinery system. In many cases, failure is preceded by specific sequences of events (signature), which can be detected by an adequate Hidden Markov Model (HMM). Classical laws like lifetime models or survival functions are used to estimate the lifetime of a system. The default of these laws is that only the elapsed time is used to estimate the end of life of a system. The aim of this paper is to validate an HMM approach. We first use a synthetic HMM model of degradation to produce event sequences. This synthetic model has been inspired by a real process. In this case, we can adjust the failure rate by changing model parameters. All the parameters of this synthetic model are known and provide references which can be evaluated by different indicators. Classical survival functions used in reliability are computed on synthetic sequences. These laws validate the behavior of the synthetic model. The higher the failure rate, the shorter the lifetime duration. These results confirm that a four-state, left to right, HMM topology can represent the degradation level of a system. In a second time, this HMM approach is used in a real case, where degradation levels are unknown. Degradation estimates are compared with the results from classical survival functions used in the first case. Then we show that the degradation level provided by the HMM approach is more efficient than the survival functions approach. The HMM approach takes into account the events collected about a system, not only the elapsed time as is the case with survival functions.

PAVEMENT CRACKING DETECTION USING AN ANISOTROPY MEASUREMENT

Automatic pavement cracking detection is a part of road maintenance and rehabilitation strategies. Cracks detection is one of the main features used by road authorities to manage efficiently its networks. Different systems are available to perform road analysis. We give a short description of some of them. Apparatus which was used to provide our images is described with more details. Road surface is made using randomly organized aggregates which can have different sizes. Scanned pictures of theses surfaces appear as random distribution of a reduced set of gray levels. Automatic crack detection is a difficult task due to the noisy pavement surface. In this paper, we introduce a measure of anisotropy for the characterization of cracks. The basic idea of this method is to detect the variation of features by considering different orientations. Noise variation and defect properties can be take into account by our method. Comparative results of anisotropy method with threshold method and 2D wavelet transform method are presented to illustrate benefits of anisotropy. We show that this method can be used to detect others types of defects, such as joints.

Statistical evaluation of Hidden Markov Models topologies, based on industrial synthetic model

Abstract Prediction of physical particular phenomenon is based on knowledges of the phenomenon. Theses knowledges help us to conceptualize this phenomenon throw different models. Hidden Markov Models (HMM) can be used for modeling complex processes. We use this kind of models as tool for fault diagnosis systems. Nowadays, industrial robots living in stochastic environment need faults detection to prevent any breakdown. In this paper, we wish to evaluate three Hidden Markov Models topologies of Vrignat et al. (2010), based on upstream industrial synthetic Hidden Markov Model. Our synthetic model gives us simulation such as real industrial Computerized Maintenance Management System. Evaluation is made by two statistical tests. Therefore, we evaluate two learning algorithms: Baum-Welch Baum et al. (1970) and segmental K-means Viterbi (1967). We also evaluate two different distributions for stochastic generation of synthetic HMM labels. After a brief introduction on Hidden Markov Model, we present some statistical tests used in current literature for model selection. Afterwards, we support our study by an example of simulated industrial process by using synthetic HMM. This paper examines stochastic parameters of the various tested models on this process, for finally come up with the most relevant model and the best learning algorithm for our predictive maintenance system.

A very simple framework for 3D human poses estimation using a single 2D image: Comparison of geometric moments descriptors

Abstract In this paper, we propose a framework in order to automatically extract the 3D pose of an individual from a single silhouette image obtained with a classical low-cost camera without any depth information. By pose, we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from simulated 3D human models publicly available on the internet. The main advantages of such approach are that silhouettes can be very easily extracted from video, and 3D human models can be animated using motion capture data in order to quickly build any movement training data. In order to match detected silhouettes with simulated silhouettes, we compared geometrics invariants moments. According to our results, we show that the proposed method provides very promising results with a very low time processing.

3D Human Poses Estimation from a Single 2D Silhouette

This work focuses on the problem of automatically extracting human 3D poses from a single 2D image. By pose we mean the configuration of human bones in order to reconstruct a 3D skeleton representing the 3D posture of the detected human. This problem is highly non-linear in nature and confounds standard regression techniques. Our approach combines prior learned correspondences between silhouettes and skeletons extracted from 3D human models. In order to match detected silhouettes with simulated silhouettes, we used Krawtchouk geometric moment as shape descriptor. We provide quantitative results for image retrieval across different action and subjects, captured from differing viewpoints. We show that our approach gives promising result for 3D pose extraction from a single silhouette.

Hidden Markov model framework for industrial maintenance activities

This article deals with modelization of industrial process by using hidden Markov model. The process is seen as a discrete event system. We propose different structures based on Markov automata, called topologies. A synthetic hidden Markov model is designed in order to match to a real industrial process. The models are intended to decode industrial maintenance observations (also called “symbol”). Symbols are produced with a corresponding degradation level (also called “state”). These 2-tuple (symbol, state) are known as Markov chains, also called “a signature.” Hence, these various 2-tuple are implemented in the proposed topologies by using the Baum–Welch learning algorithm (decoding by forward variable) and the segmental K-means learning (decoding by Viterbi). We assess different frameworks (topology, learning and decoding algorithm, distribution) by relevancy measurements on model outputs. Then, we determine the most relevant framework for use in maintenance activities. Afterward, we try to minimize the size of the learning data. Thus, we could evaluate the model by using “sliding windows” of data. Finally, an industrial application is developed and compared with this framework. Our goal is to improve worker safety, maintenance policy, process reliability and reduce CO2 emissions in the industrial sector.